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A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms

An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentatio...

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Autores principales: Tao, Xingxiang, Dang, Hao, Zhou, Xiaoguang, Xu, Xiangdong, Xiong, Danqun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9174536/
https://www.ncbi.nlm.nih.gov/pubmed/35692314
http://dx.doi.org/10.3389/fpubh.2022.892418
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author Tao, Xingxiang
Dang, Hao
Zhou, Xiaoguang
Xu, Xiangdong
Xiong, Danqun
author_facet Tao, Xingxiang
Dang, Hao
Zhou, Xiaoguang
Xu, Xiangdong
Xiong, Danqun
author_sort Tao, Xingxiang
collection PubMed
description An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentation while ignoring the computational cost. However, performing such segmentation networks requires a high-performance device with a powerful GPU and a high bandwidth memory. To address this issue, in this study, a lightweight deep learning network is developed for a better balance between computational cost and segmentation accuracy. We have made two efforts in designing the network. On the one hand, we adopt bottleneck residual blocks to replace the internal components in the encoder and decoder of the traditional U-Net to make the network more lightweight. On the other hand, we embed the two attention modules to model long-range dependencies in spatial and channel dimensions for the accuracy of segmentation. In addition, we employ Top-hat transforms and contrast-limited adaptive histogram equalization (CLAHE) as the pre-processing strategy to enhance the coronary arteries to further improve the accuracy. Experimental evaluations conducted on the coronary angiograms dataset show that the proposed lightweight network performs well for accurate coronary artery segmentation, achieving the sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.8770, 0.9789, 0.9729, and 0.9910, respectively. It is noteworthy that the proposed network contains only 0.75 M of parameters, which achieves the best performance by the comparative experiments with popular segmentation networks (such as U-Net with 31.04 M of parameters). Experimental results demonstrate that our network can achieve better performance with an extremely low number of parameters. Furthermore, the generalization experiments indicate that our network can accurately segment coronary angiograms from other coronary angiograms' databases, which demonstrates the strong generalization and robustness of our network.
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spelling pubmed-91745362022-06-09 A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms Tao, Xingxiang Dang, Hao Zhou, Xiaoguang Xu, Xiangdong Xiong, Danqun Front Public Health Public Health An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentation while ignoring the computational cost. However, performing such segmentation networks requires a high-performance device with a powerful GPU and a high bandwidth memory. To address this issue, in this study, a lightweight deep learning network is developed for a better balance between computational cost and segmentation accuracy. We have made two efforts in designing the network. On the one hand, we adopt bottleneck residual blocks to replace the internal components in the encoder and decoder of the traditional U-Net to make the network more lightweight. On the other hand, we embed the two attention modules to model long-range dependencies in spatial and channel dimensions for the accuracy of segmentation. In addition, we employ Top-hat transforms and contrast-limited adaptive histogram equalization (CLAHE) as the pre-processing strategy to enhance the coronary arteries to further improve the accuracy. Experimental evaluations conducted on the coronary angiograms dataset show that the proposed lightweight network performs well for accurate coronary artery segmentation, achieving the sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.8770, 0.9789, 0.9729, and 0.9910, respectively. It is noteworthy that the proposed network contains only 0.75 M of parameters, which achieves the best performance by the comparative experiments with popular segmentation networks (such as U-Net with 31.04 M of parameters). Experimental results demonstrate that our network can achieve better performance with an extremely low number of parameters. Furthermore, the generalization experiments indicate that our network can accurately segment coronary angiograms from other coronary angiograms' databases, which demonstrates the strong generalization and robustness of our network. Frontiers Media S.A. 2022-05-25 /pmc/articles/PMC9174536/ /pubmed/35692314 http://dx.doi.org/10.3389/fpubh.2022.892418 Text en Copyright © 2022 Tao, Dang, Zhou, Xu and Xiong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Tao, Xingxiang
Dang, Hao
Zhou, Xiaoguang
Xu, Xiangdong
Xiong, Danqun
A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
title A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
title_full A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
title_fullStr A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
title_full_unstemmed A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
title_short A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
title_sort lightweight network for accurate coronary artery segmentation using x-ray angiograms
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9174536/
https://www.ncbi.nlm.nih.gov/pubmed/35692314
http://dx.doi.org/10.3389/fpubh.2022.892418
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